Dynamic input granularity estimation for network path forecasting using timeseries features

Abstract

In one embodiment, a device identifies peaks of a timeseries of a path metric used to predict performance of a path in a network. The device determines one or more characteristics of the peaks of the timeseries. The device computes, based on the one or more characteristics of the peaks, a measurement frequency for the path metric. The device causes the path metric to be measured in the network according to the measurement frequency.

1 . A method comprising:
 identifying, by a device, peaks of a timeseries of a path metric used to predict performance of a path in a network;   determining, by the device, one or more characteristics of the peaks of the timeseries;   computing, by the device and based on the one or more characteristics of the peaks, a measurement frequency for the path metric that is lower than a current measurement frequency for the path metric; and   presenting, by the device and for display, an indication of the measurement frequency for the path metric via a user interface.   
     
     
         2 . The method as in  claim 1 , wherein the peaks of the timeseries identified by the device satisfy an imposed minimum peak height or a maximum peak width. 
     
     
         3 . The method as in  claim 1 , wherein the one or more characteristics of the peaks of the timeseries indicate whether the peaks of the timeseries are periodic or aperiodic. 
     
     
         4 . The method as in  claim 1 , wherein identifying the peaks of the timeseries comprises:
 excluding a fluctuation in the timeseries as a peak based on a required minimum amount of time between peaks.   
     
     
         5 . The method as in  claim 1 , wherein the one or more characteristics of the peaks of the timeseries indicate whether the peaks of the timeseries are preceded by patterns that signal that a peak is imminent. 
     
     
         6 . The method as in  claim 1 , wherein the path metric is used to predict performance of the path by a prediction model of a routing engine that reroutes traffic conveyed via the path onto another path in the network in advance of a predicted degradation of the path metric. 
     
     
         7 . The method as in  claim 6 , wherein the device computes the measurement frequency based further in part on an accuracy measurement for the prediction model. 
     
     
         8 . The method as in  claim 1 , further comprising:
 computing, by the device and based on the one or more characteristics of the peaks, a length of history of the path metric to be retained.   
     
     
         9 . The method as in  claim 1 , further comprising:
 identifying, by the device, a second path in the network as being similar to that of the path; and   computing, by the device and based on the measurement frequency, a second measurement frequency for the second path.   
     
     
         10 . The method as in  claim 1 , wherein the indication comprises an option to change the current measurement frequency to the measurement frequency. 
     
     
         11 . An apparatus, comprising:
 one or more network interfaces;   a processor coupled to the one or more network interfaces and configured to execute one or more processes; and   a memory configured to store a process that is executable by the processor, the process when executed configured to:
 identify peaks of a timeseries of a path metric used to predict performance of a path in a network; 
 determine one or more characteristics of the peaks of the timeseries; 
 compute, based on the one or more characteristics of the peaks, a measurement frequency for the path metric that is lower than a current measurement frequency for the path metric; and 
 present, to a display, an indication of the measurement frequency for the path metric via a user interface. 
   
     
     
         12 . The apparatus as in  claim 11 , wherein the peaks of the timeseries identified by the apparatus satisfy an imposed minimum peak height or a maximum peak width. 
     
     
         13 . The apparatus as in  claim 11 , wherein the one or more characteristics of the peaks of the timeseries indicate whether the peaks of the timeseries are periodic or aperiodic. 
     
     
         14 . The apparatus as in  claim 11 , wherein the apparatus identifies the peaks of the timeseries by:
 excluding a fluctuation in the timeseries as a peak based on a required minimum amount of time between peaks.   
     
     
         15 . The apparatus as in  claim 11 , wherein the one or more characteristics of the peaks of the timeseries indicate whether the peaks of the timeseries are preceded by patterns that signal that a peak is imminent. 
     
     
         16 . The apparatus as in  claim 11 , wherein the path metric is used to predict performance of the path by a prediction model of a routing engine that reroutes traffic conveyed via the path onto another path in the network in advance of a predicted degradation of the path metric. 
     
     
         17 . The apparatus as in  claim 16 , wherein the apparatus computes the measurement frequency based further in part on an accuracy measurement for the prediction model. 
     
     
         18 . The apparatus as in  claim 11 , wherein the process when executed is further configured to:
 compute, based on the one or more characteristics of the peaks, a length of history of the path metric to be retained.   
     
     
         19 . The apparatus as in  claim 11 , wherein the process when executed is further configured to:
 identify a second path in the network as being similar to that of the path; and   compute, based on the measurement frequency, a second measurement frequency for the second path.   
     
     
         20 . A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:
 identifying, by the device, peaks of a timeseries of a path metric used to predict performance of a path in a network;   determining, by the device, one or more characteristics of the peaks of the timeseries;   computing, by the device and based on the one or more characteristics of the peaks, a measurement frequency for the path metric that is lower than a current measurement frequency for the path metric; and   presenting, by the device and for display, an indication of the measurement frequency for the path metric via a user interface.